12,861 Lead Data Scientist jobs in India

Primary Responsibilities:

Ensure traceability and versioning of datasets, models & evaluation pipelines

· Design, prototype, build and maintain APIs for consumption of machine learning models at scale.

· Facilitate the development and deployment of POC machine learning systems

· Perform Data cleansing activities such as data cleaning, merging and enrichment etc.

· Perform feature engineering through extracting meaningful features from measured and/or derived data

· Perform exploratory and targeted data analyses to get key insights

· Build Stochastic and Machine learning algorithms that potentially address business problems

· Using standard methodologies framework to ensure data quality and reconciliation checks are in place and are transparent to everyone

- Interact with Business and create smart signals for VRM/RMs communication with customers

- Set up the process and continuous monitoring the performance of signals and VRMs and RM agents

Development of models & tools

- Implementation & effective usage of models

- Delivering models as per Business Alignment and expected quality.

- Design & Creation of campaign strategies independently.

- Tracking of models performance as per expectation

Role: Lead AI/ML Engineer

Location: Ahmedabad, Pune, Indore

Years of Experience: 8 to 12 years

Notice Period – 0-45 Days Max

We are looking for a highly skilled and experienced Lead AI/ML Engineer to lead the development and deployment of machine learning solutions across the organization. The ideal candidate will possess deep technical expertise in ML algorithms, cloud platforms (Azure), large language models (LLMs), and strong engineering practices. You will play a key role in designing scalable AI/ML systems, mentoring team members, and integrating advanced AI solutions into enterprise workflows.

Key Responsibilities:

• Lead the end-to-end design and development of machine learning models, from data preprocessing to deployment and monitoring in production.
• Architect and implement scalable MLOps pipelines using Azure ML, Azure Databricks, MLflow, and CI/CD workflows.
• Design and deploy LLM-based solutions, including Retrieval-Augmented Generation (RAG) and prompt engineering for enterprise use cases.
• Collaborate with product managers, data engineers, and business stakeholders to align AI solutions with organizational goals.
• Create insightful dashboards and visualizations using Power BI for model monitoring and business reporting.
• Apply software engineering and design principles to ensure reusable, testable, and maintainable ML code.
• Stay up to date with advancements in AI/ML, especially in Generative AI, Agentic AI and LLMs, and evaluate them for practical application.
• Review team deliverables, provide constructive feedback, and drive technical decision-making and architecture reviews.
• Contribute to the organization’s AI/ML roadmap, guiding adoption of new tools, frameworks, and research-driven techniques.
• Mentor junior team members, conduct code reviews, and lead knowledge-sharing sessions.

Required Skills and Experience:

• 8+ years of experience in Machine Learning, Software Development, and Data Analytics.
• Proficient in Python, SQL, and C#, with a strong background in software engineering.
• Hands-on experience with Azure ecosystem (Azure ML, Azure Data Factory, Azure Functions).
• Strong experience in Azure Databricks, Spark, and distributed data processing.
• Expertise in LLM integration, RAG pipelines, and prompt engineering for generative AI applications.
• Deep understanding of statistical modeling, hypothesis testing, and data interpretation.
• Solid foundation in MLOps, model monitoring, drift detection, and pipeline automation.
• Strong understanding of software engineering and design patterns in ML system development.
• Proficient in Power BI for visualization and reporting.

Preferred Certifications (Good to have):

• Microsoft Certified: Azure Data Scientist Associate

What We Offer:

• Competitive Salary
• Work Life Balance
• Professional Development
• Innovative Work Environment.
• Recognition and Rewards

Role : Senior Data Scientist / Lead Data Scientist

About Media.net :

Media.net is a leading, global ad tech company that focuses on creating the most transparent and efficient path for advertiser budgets to become publisher revenue. Our proprietary contextual technology is at the forefront of enhancing Programmatic buying, the latest industry-standard in ad buying for digital platforms.

The Media.net platform powers major global publishers and ad-tech businesses at scale across ad formats like display, video, mobile, native, as well as search. Media.net’s U.S. HQ is based in New York, and the Global HQ is in Dubai. With office locations and consultant partners across the world, Media.net takes pride in the value-add it offers to its 50+ demand and 21K+ publisher partners, in terms of both products and services.

Data Science is at the heart of Media.net. The team uses advanced statistical and machine learning and deep learning models, large scale distributed computing along with tools from mathematics, economics, auction theory to build solutions that enable us to match users with relevant ads in the most optimal way thereby maximizing revenue for our customers and for Media.net.

Some of the challenges the team deals with:

• How do you use information retrieval, machine learning models to estimate click through rate and revenue given the information regarding the position of the slot, user device, location and content of the page. How do you scale the same for thousands of domains, millions of urls?
• How do you match ads to page views considering contextual information? How do you design learning mechanisms to continuously learn from user feedback in the form of clicks and conversions? How do you deal with the extremely sparse data? What do you do for new ads and new pages? How do we design better explore-exploit frameworks? How do you design learning algorithms that are fast and scalable?
• How do you combine contextual targeting with behavioral user-based targeting?
• How do you establish a unique user identity based on multiple noisy signals so that behavioral targeting is accurate?
• Can you use NLP to find more genetic trends based on the content of the page and as?

What is in it for you?

• Understand business requirements, analyze and extract relevant information from large amounts of historical data.
• Use your knowledge of Information retrieval, NLP, Machine Learning (including Deep Learning) to build prototype solutions keeping scale, speed and accuracy in mind.
• Work with engineering teams to implement the prototype. Work with engineers to design appropriate model performance metrics and create reports to track the same.
• Work with the engineering teams to identify areas of improvement, jointly develop research agenda and execute on the same using cutting edge algorithms and tools.
• You will need to understand a broad range of ML algorithms and appreciation on how to apply them to complex practical problems. You will also need to have enough theoretical background and a good grasp of algorithms to be able to critically evaluate existing ML algorithms and be creative when there is a need to go beyond textbook solutions.

Who should apply for this role ?

• PhD/Research Degree or BS/MS in Computer Science, Statistics, Artificial Intelligence, Machine learning, Operations Research or related field.
• 6 - 8 years of experience in building Machine Learning/AI/Information Retrieval models
• Extensive knowledge and practical experience in machine learning, data mining, artificial intelligence, statistics.
• Understanding of supervised and unsupervised algorithms including but not limited to linear models, decision trees, random forests, gradient boosting machines etc.
• Excellent analytical and problem-solving abilities.
• Good knowledge of scientific programming in Python.
• Experience with Apache Spark is desired.
• Excellent verbal & written communication skills

Bonus Points:

• Publications or presentation in recognized Machine Learning and Data Mining journals/conferences such as ICML
• Knowledge in several of the following: Math/math modeling, decision theory, fuzzy logic, Bayesian techniques, optimization techniques, statistical analysis of data, information retrieval, natural language processing, large scale data processing and data mining
• Ability deal with ambiguity & break them down into research problems
• Strong theoretical and research acumen

Licious is a fast-paced, innovative D2C brand revolutionizing the meat and seafood industry in India. We leverage cutting-edge technology, data science, and customer insights to deliver unmatched quality, convenience, and personalization. Join us to solve complex problems at scale and drive data-driven decision-making!

Role Overview:

We are seeking a Lead Data Scientist with 6+ years of experience to build and deploy advanced ML models (LLMs, Recommendation Systems, Demand Forecasting) and generate actionable insights. You will collaborate with cross-functional teams (Product, Supply Chain, Marketing) to optimize customer experience, demand prediction, and business growth.

Key Responsibilities:

1. Machine Learning & AI Solutions:

• Develop and deploy Large Language Models (LLMs) for customer support automation, personalized content generation, and sentiment analysis.
• Enhance Recommendation Systems (collaborative filtering, NLP-based, reinforcement learning) to drive engagement and conversions.
• Build scalable Demand Forecasting models (time series, causal inference) to optimize inventory and supply chain.

2. Data-Driven Insights:

• Analyze customer behavior, transactional data, and market trends to uncover growth opportunities.
• Create dashboards and reports (using Tableau/Power BI) to communicate insights to stakeholders.

3. Cross-Functional Collaboration:

• Partner with Engineering to productionize models (MLOps, APIs, A/B testing).
• Work with Marketing to design hyper-personalized campaigns using CLV, churn prediction, and segmentation.

4. Innovation & Scalability:

• Stay updated with advancements in GenAI, causal ML, and optimization techniques.
• Improve model performance through feature engineering, ensemble methods, and experimentation.

Qualifications:

• Education: BTech/MTech/MS/Ph.D. in Computer Science, Statistics, or related fields.
• Experience: 6+ years in Data Science, with hands-on expertise in:
• LLMs (GPT, BERT, fine-tuning, prompt engineering).
• Recommendation Systems (matrix factorization, neural CF, graph-based).
• Demand Forecasting (ARIMA, Prophet, LSTM, Bayesian methods).
• Python/R , SQL, PySpark, and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Cloud platforms (AWS/GCP) and MLOps tools (MLflow, Kubeflow).